Another important consideration for the \(\mu \) AGC design is the cyber security. While the distributed and decentralized \(\mu \) AGC reduce the cyber risks of the \(\mu \) AGC by sparsifying or eliminating long-distance communication links among DERs, these \(\mu \) AGC design may be still susceptible to the false data injection (FDI) attack where the sensors can be manipulated to report wrong information to the \(\mu \) AGC. Many references concerns attenuating the influence of the FDI attacks [1]}. One category of methods explores state estimation techniques [2]}, for example, the Kalman filter method [3]} and optimization-based state estimation methods [4]}. These methods either rely on the accuracy of measurements or heavy computations [5]}. To address these challenges, observer-based detection and control methods have been proposed [1]}, [7]}. Recently, the unknown input observer (UIO) has found a broad range of applications in FDI attack detection and cyber-resilient control designs [8]}, [9]} since it can separate the external disturbance from the attack signal, yielding a more sensitive anomaly diagnosis. However, these cyber security solutions are built upon existing \(\mu \) AGC designs that require accurate frequency information. It is an open-ended question whether these solutions can extend to other control designs that require different information other than frequencies. While some cyber attack solutions [10]}, [11]} are designed for a general model that serves as an abstraction of many engineering systems, e.g., a state-space model, the model that should be used for the purpose of developing cyber solutions heavily depends on the information feeding its controller. For a specific controller, it might not be straightforward to derive a proper model that lends itself to the cyber solution design even for domain experts. Therefore, the development of \(\mu \) AGC and its cyber solution are interdependent.
